/Highway-Driving

Path Planning Project in the Udacity's Self Driving Car Engineer Course

Primary LanguageC++MIT LicenseMIT

CarND-Path-Planning-Project

Self-Driving Car Engineer Nanodegree Program

Simulator.

You can download the Term3 Simulator which contains the Path Planning Project from the [releases tab (https://github.com/udacity/self-driving-car-sim/releases/tag/T3_v1.2).

To run the simulator on Mac/Linux, first make the binary file executable with the following command:

sudo chmod u+x {simulator_file_name}

Goals

In this project the goal is to safely navigate around a virtual highway with other traffic that is driving +-10 MPH of the 50 MPH speed limit. The car's localization and sensor fusion data are provided and there is also a sparse map list of waypoints around the highway. The car tries to go as close as possible to the 50 MPH speed limit, which means passing slower traffic when possible. Also, other cars too will try to change lanes. The car avoids hitting other cars at all cost as well as driving inside of the marked road lanes at all times, unless going from one lane to another. The car should be able to make one complete loop around the 6946m highway. Since the car is trying to go 50 MPH, it should take a little over 5 minutes to complete 1 loop. Also the car does not experience total acceleration over 10 m/s^2 and jerk that is greater than 10 m/s^3.

The map of the highway is in data/highway_map.txt

Each waypoint in the list contains [x,y,s,dx,dy] values. x and y are the waypoint's map coordinate position, the s value is the distance along the road to get to that waypoint in meters, the dx and dy values define the unit normal vector pointing outward of the highway loop.

The highway's waypoints loop around so the frenet s value, distance along the road, goes from 0 to 6945.554.

Basic Build Instructions

  1. Clone this repo.
  2. Make a build directory: mkdir build && cd build
  3. Compile: cmake .. && make
  4. Run it: ./path_planning.

Here is the data provided from the Simulator to the C++ Program

Main car's localization Data (No Noise)

["x"] The car's x position in map coordinates

["y"] The car's y position in map coordinates

["s"] The car's s position in frenet coordinates

["d"] The car's d position in frenet coordinates

["yaw"] The car's yaw angle in the map

["speed"] The car's speed in MPH

Previous path data given to the Planner

//Note: Return the previous list but with processed points removed, can be a nice tool to show how far along the path has processed since last time.

["previous_path_x"] The previous list of x points previously given to the simulator

["previous_path_y"] The previous list of y points previously given to the simulator

Previous path's end s and d values

["end_path_s"] The previous list's last point's frenet s value

["end_path_d"] The previous list's last point's frenet d value

Sensor Fusion Data, a list of all other car's attributes on the same side of the road. (No Noise)

["sensor_fusion"] A 2d vector of cars and then that car's [car's unique ID, car's x position in map coordinates, car's y position in map coordinates, car's x velocity in m/s, car's y velocity in m/s, car's s position in frenet coordinates, car's d position in frenet coordinates.

Details

  1. The car uses a perfect controller and will visit every (x,y) point it recieves in the list every .02 seconds. The units for the (x,y) points are in meters and the spacing of the points determines the speed of the car. The vector going from a point to the next point in the list dictates the angle of the car. Acceleration both in the tangential and normal directions is measured along with the jerk, the rate of change of total Acceleration. The (x,y) point paths that the planner recieves should not have a total acceleration that goes over 10 m/s^2, also the jerk should not go over 50 m/s^3. (NOTE: As this is BETA, these requirements might change. Also currently jerk is over a .02 second interval, it would probably be better to average total acceleration over 1 second and measure jerk from that.

  2. There will be some latency between the simulator running and the path planner returning a path, with optimized code usually its not very long maybe just 1-3 time steps. During this delay the simulator will continue using points that it was last given, because of this its a good idea to store the last points you have used so you can have a smooth transition. previous_path_x, and previous_path_y can be helpful for this transition since they show the last points given to the simulator controller with the processed points already removed. You would either return a path that extends this previous path or make sure to create a new path that has a smooth transition with this last path.

Solution

The project was solved in 2 steps. First step was to plan the lane change behavior of the car. Then, the trjectory was generated for upto the next 3 waypoints due to the latency of the sensors. The lanes are numbered 0, 1, 2 representing left, middle and right lanes respectively.

The lane change behavior of the vehicle is planned in such a way that the vehicle moves to the middle lane in the absence of any vehicle in that lane. In this way, the vehicle would have an option to overtake on either sides whereas if it were in one of the edge lanes, it would be constrained to move to the middle lane only. The sensor fusion output values are stored in a variable named "sensor_fusion". The closest vehicle behind the vehicle and ahead of the vehicle in each lane are obtained along with their speed and the location values. The expected distance of the vehicle behind our vehicle after 2 seconds is calculated. It is assumed that an overtaking maneuver will take 2 seconds. Using these values, the requirement of a lane change is decided with a preferential treatment given to the middle lane. When a vehicle is at a distance less than 60m in lane 0 or 2, the possibility of an overtaking maneuver to lane 1 is checked. The distance of the vehicles ahead and the expected distance of the vehicle behind should be atleast 30m away from this vehicle. A lane change to the middle lane irrespective of a vehicle presence in the current lane is performed when there is no vehicle 120m ahead and there is no vehicle expected 30m behind our vehicle after 2 seconds. If the current lane is the middle lane, when a vehicle is at a distance less than 30m, the possibility of an overtaking maneuver is checked. Overtaking is performed when there is no vehicle at a 60m distance ahead or when there is no vehicle 45m ahead and the vehicle beyond that not slower than the middle lane. Also, the expected distance of the vehicle behind should be greater than 30m. An automatic lane change is performed to the side lanes (0 or 2) when either of them has no vehicle for the next 180m and no vehicle will be below 30m behind after 2 seconds.

Safety is taken care of by reducing the speed by 0.224 mph (0.1 m/s) per iteration (0.02 seconds) when a vehicle is slow and is within a 30m distance from this vehicle. This gives us an acceleration of 5 m/s^2. When the vehicle becomes faster than this vehicle, this vehicle also increases its speed by 0.112 mph (0.05 m/s) per iteration. When there is no vehicle in front, the vehicle gains back its speed upto 49.5 mph by accelerating at the rate of 5 m/s^2. The code corresponding to the behavioral planning module is from lines 269 to 402.

The trajectory is calculated using spline library for smoother curves. 3 waypoints that are 30m apart are calculated in every iteration using splines and are pushed.

The code ran without any error making overtaking maneuvers whenever required and the vehicle finished a lap in around 5 minutes.

Tips

A really helpful resource for doing this project and creating smooth trajectories was using http://kluge.in-chemnitz.de/opensource/spline/, the spline function is in a single hearder file is really easy to use.

Dependencies

Editor Settings

We've purposefully kept editor configuration files out of this repo in order to keep it as simple and environment agnostic as possible. However, we recommend using the following settings:

  • indent using spaces
  • set tab width to 2 spaces (keeps the matrices in source code aligned)

Code Style

Please (do your best to) stick to Google's C++ style guide.

Project Instructions and Rubric

Note: regardless of the changes you make, your project must be buildable using cmake and make!

Call for IDE Profiles Pull Requests

Help your fellow students!

We decided to create Makefiles with cmake to keep this project as platform agnostic as possible. Similarly, we omitted IDE profiles in order to ensure that students don't feel pressured to use one IDE or another.

However! I'd love to help people get up and running with their IDEs of choice. If you've created a profile for an IDE that you think other students would appreciate, we'd love to have you add the requisite profile files and instructions to ide_profiles/. For example if you wanted to add a VS Code profile, you'd add:

  • /ide_profiles/vscode/.vscode
  • /ide_profiles/vscode/README.md

The README should explain what the profile does, how to take advantage of it, and how to install it.

Frankly, I've never been involved in a project with multiple IDE profiles before. I believe the best way to handle this would be to keep them out of the repo root to avoid clutter. My expectation is that most profiles will include instructions to copy files to a new location to get picked up by the IDE, but that's just a guess.

One last note here: regardless of the IDE used, every submitted project must still be compilable with cmake and make./

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